\begin{table}[t]
\centering
\caption{\textbf{Effect of Voter ID Law on 2016 General Election Turnout, by Pre-Treatment Turnout.}
\label{tab:general_heterogeneity}}
\begin{tabular}{l|ccccc}
\toprule \toprule
 & \multicolumn{5}{c}{\# of Pre-Treatment General Elections}\\
 & 0 & 1 & 2 & 3 & 4 \\
\midrule
 \# of Pre-Treatment Primary Elections & & & & \medskip \\
0  & -0.023 & -0.050 & -0.045 & -0.038 & -0.016 \\
 & (0.000) & (0.000) & (0.001) & (0.001) & (0.001) \smallskip\\
1  & -0.039 & -0.060 & -0.055 & -0.039 & -0.014 \\
 & (0.002) & (0.002) & (0.002) & (0.002) & (0.000) \smallskip\\
2  & -0.048 & -0.047 & -0.054 & -0.039 & -0.012 \\
 & (0.010) & (0.006) & (0.004) & (0.002) & (0.000) \smallskip\\
3  & 0.005 & -0.073 & -0.045 & -0.040 & -0.009 \\
 & (0.005) & (0.026) & (0.009) & (0.003) & (0.000) \smallskip\\
4  &  & -0.057 & -0.031 & -0.022 & -0.008 \\
 &  & (0.101) & (0.021) & (0.002) & (0.000) \smallskip\\
\bottomrule \bottomrule
\multicolumn{6}{p{0.9\textwidth}}{\footnotesize Each cell estimates the effect of the voter ID law on 
2016 primary turnout, estimating the effect separately for different pre-treatment turnout patterns.  
We construct strata of treated and control units based on the total number of times a voter casted a ballot 
in a pre-treatment primary election (2008-2014) and pre-treatment general election (2008-2014).  
We implement the same exact matching procedure described in Section \ref{sec:primary_effect}.  
Robust standard errors are in parentheses.}
\end{tabular}
\end{table}
